A Constrained Optimization Approach to Globally Consistent Mapping - Robotics Institute Carnegie Mellon University

A Constrained Optimization Approach to Globally Consistent Mapping

Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, pp. 564 - 569, September, 2002

Abstract

Mobile robot localization from large-scale appearance mosaics has been showing increasing promise as a low-cost, high-performance and infrastructure free solution to vehicle-guidance in man-made environments. The generation of the globally consistent high-resolution mosaics crucial to this procedure suffers from the same problem of loop-closure in cyclic environments that is commonly encountered in all map-building procedures. This paper presents a batch solution to the problem of reliably generating globally consistent mosaics at low computational cost, that simultaneously exploits the topological constraints among the observations and minimizes the total residual in observed features. An extension to a general scalable framework that facilitates an incremental online mapping strategy is also presented, along with results using simulated data and from real indoor environments.

BibTeX

@conference{Unnikrishnan-2002-8556,
author = {Ranjith Unnikrishnan and Alonzo Kelly},
title = {A Constrained Optimization Approach to Globally Consistent Mapping},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2002},
month = {September},
volume = {1},
pages = {564 - 569},
}